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| Image Super-resolution Reconstruction Based on Kernel Partial Least Squares and Weighted Boosting |
| Li Xiao-yan He Hong-jie Yin Zhong-ke Chen Fan |
| Sichuan Key Laboratory of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China |
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Abstract The Kernel Partial Least Squares (KPLS) method has a large calculation since it uses all the principal components for each image block. To consider reconstruction quality and time efficiency, a weighted Boosting based algorithm is proposed in this paper. To choose adaptively the best number of principal components for each image block, the estimator in KPLS prediction model is performed for compensation. The weight coefficient expression of compensation is deduced. The reconstruction effects in different Boosting threshold are discussed. With an appropriate threshold, the chosen best number of principal components can better satisfy KPLS regression model accuracy. Experimental results demonstrate that the proposed method outperforms the conventional methods in super-resolution reconstructed quality.
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Received: 16 November 2011
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Corresponding Authors:
He Hong-jie
E-mail: lixiaoyan7015@yahoo.cn; hehojie@126.com
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2012, Vol. 34 
